GradFunc.h

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#ifndef GRADFUNC_H_
#define GRADFUNC_H_

#include "VecMat.h"
#include "GeneralObjective.h"

namespace ublas = boost::numeric::ublas;

//! Calculate the model gradient vector df(m)/dm 
gplib::rvec CalcGradient(GeneralObjective &Objective, const gplib::rvec &Model);
//! Calculate the model sensitivity matrix 
gplib::rmat CalcPartialDerivs(GeneralObjective &Objective, const gplib::rvec &Model, const gplib::rvec varfactor);

//! The class ModelAnalysis is used to calculate resolution matrix, nullspace and other parameters for model analyis

/*! We assume that the data characteristics as indicated by the CGeneralObjective object do not change, but we want
 * the possibility to analyse several different models
 */
class ModelAnalysis{
        private:
                bool haveModel;
                bool haveSVD;
                bool changedThreshold;
                double SVDThreshold;
                GeneralObjective &Objective;
                gplib::rvec Model;
                gplib::rmat OldSensitivityMatrix;
                gplib::rmat SensitivityMatrix;
                gplib::rmat SensitivityInv;
                gplib::rvec s;
                gplib::rmat u, vt;
                gplib::rmat vnull,unull;
                gplib::rmat sinv;
                gplib::rvec varfactor;
                void CalcSensitivityMatrix();
                void CalcSVD();
                void ApplyThreshold();
        public:
                void SetVariationFactor(const gplib::rvec varfac){varfactor = varfac;}
                void SetModel(const gplib::rvec &TheModel);
                void SetRelativeSVDThreshold(const double t){changedThreshold = true; SVDThreshold = t; }
                const gplib::rmat &GetSensitivityMatrix(){CalcSensitivityMatrix(); return SensitivityMatrix;} 
                const gplib::rmat &GetSensitivityInverse() const {return SensitivityInv;}
                gplib::rmat GetModelCorrelation(const  gplib::rmat &DataCovar);
                gplib::rmat GetModelResolution();
                gplib::rmat GetDataResolution();
                gplib::rmat GetModelCovariance(const  gplib::rmat &DataCovar);
                gplib::rmat GetModelNullspace();
                gplib::rvec GetSingularValues();
                ModelAnalysis(GeneralObjective &TheObjective):haveModel(false),haveSVD(false),changedThreshold(true),Objective(TheObjective),SVDThreshold(0.0){};
                ~ModelAnalysis(){};
};
#endif /*GRADFUNC_H_*/

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